Frank Slyne

h-index13

2papers

2 Papers

NIAug 4, 2023

Self-Normalizing Neural Network, Enabling One Shot Transfer Learning for Modeling EDFA Wavelength Dependent Gain

Agastya Raj, Zehao Wang, Frank Slyne et al.

We present a novel ML framework for modeling the wavelength-dependent gain of multiple EDFAs, based on semi-supervised, self-normalizing neural networks, enabling one-shot transfer learning. Our experiments on 22 EDFAs in Open Ireland and COSMOS testbeds show high-accuracy transfer-learning even when operated across different amplifier types.

LGMar 21, 2025

Multi-Span Optical Power Spectrum Evolution Modeling using ML-based Multi-Decoder Attention Framework

Agastya Raj, Zehao Wang, Frank Slyne et al.

We implement a ML-based attention framework with component-specific decoders, improving optical power spectrum prediction in multi-span networks. By reducing the need for in-depth training on each component, the framework can be scaled to multi-span topologies with minimal data collection, making it suitable for brown-field scenarios.